Researchers at the University of Pennsylvania and Stony Brook University have created an algorithm that can help diagnose depression at least three months earlier than health services by analyzing Facebook posts.

Their study was published Monday in the Proceedings of the National Academy of Sciences.

The machine learning algorithm performed just as well as existing screening questionnaires used to identify depression by scouring Facebook for “linguistic red flags,” including mentions of hostility and loneliness and words like “tears” and “feelings. ” Other red flags included the use of more first-person pronouns like “I” and “me.”

“What people write in social media and online captures an aspect of life that’s very hard in medicine and research to access otherwise,” says H. Andrew Schwartz, senior paper author and a principal investigator of the World Well-Being Project (WWBP). “It’s a dimension that’s relatively untapped compared to biophysical markers of disease. Considering conditions such as depression, anxiety, and PTSD, for example, you find more signals in the way people express themselves digitally.”

The WWBP, based in Penn’s Positive Psychology Center and Stony Brook’s Human Language Analysts Lab, studies how the words people use reflect inner feelings and contentedness.

"Social media data contain markers akin to the genome," said Johannes Eichstaedt, a founding research scientist at WWBP. "With surprisingly similar methods to those used in genomics, we can comb social media data to find these markers. Depression appears to be something quite detectable in this way; it really changes people's use of social media in a way that something like skin disease or diabetes doesn't."

Researchers at the University of Pennsylvania and Stony Brook University have created an algorithm that can help diagnose depression at least three months earlier than health services by analyzing Facebook posts....